We explore the effects of increasing the feature pyramid [1]} capacity to enhance the representation learned upon the frozen backbone features. To increase the FPN capacity, we simply repeat the lateral and top-down connections of FPN \(N\)  times before applying the post-hoc convolution. We insert a ReLU and BatchNorm layer in each lateral connection, and add a skip connection from the backbone feature maps to every level (i.e. \(1, 2, ..., N\) ). The post-hoc convolution and BatchNorm layers are kept the same. All runs are repeated 5 times and trained for 46.1k steps following the same protocol as the LVIS benchmark of the manuscript.
